Can we use logistic regression for classification of non linear data if not why?
Logistic Regression has traditionally been used as a linear classifier, i.e. when the classes can be separated in the feature space by linear boundaries. That can be remedied however if we happen to have a better idea as to the shape of the decision boundary…
How do you handle non linear data?
The easiest approach is to first plot out the two variables in a scatter plot and view the relationship across the spectrum of scores. That may give you some sense of the relationship. You can then try to fit the data using various polynomials or splines.
Why is LR used for binary classification problems?
LR is a model used for only binary classification problems and it performs well on linearly separable classes. The biggest assumption in LR is that it assumes that the data is linearly separable (which can be separated by line) which is very rare in real life problems.
How is logistic regression used as a nonlinear classifier?
Logistic regression has traditionally been used to come up with a hyperplane that separates the feature space into classes. But if we suspect that the decision boundary is nonlinear we may get better results by attempting some nonlinear functional forms for the logit function.
Which is the most common assumption in LR?
The biggest assumption in LR is that it assumes that the data is linearly separable (which can be separated by line) which is very rare in real life problems. The task is to find decision boundary which separates the 2 classes (+ve and -ve) from each other.
Do you use linear decision boundary in logistic regression?
While logistic regression makes core assumptions about the observations such as IID (each observation is independent of the others and they all have an identical probability distribution), the use of a linear decision boundary is not one of them.